دورية أكاديمية
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Large-scale evaluation of automated clinical note de-identification and its impact on information extraction

التفاصيل البيبلوغرافية
العنوان: Large-scale evaluation of automated clinical note de-identification and its impact on information extraction
المؤلفون: Deleger, Louise, Molnar, Katalin, Savova, Guergana, Xia, Fei, Lingren, Todd, Li, Qi, Marsolo, Keith, Jegga, Anil, Kaiser, Megan, Stoutenborough, Laura, Solti, Imre
بيانات النشر: BMJ Group
سنة النشر: 2012
المجموعة: Harvard University: DASH - Digital Access to Scholarship at Harvard
مصطلحات موضوعية: Natural language processing, privacy of patient data, health insurance portability and accountability act, automated de-identification, protected health information, NLP, text mining, human-computer interaction and human-centered computing, providing just-in-time access to the biomedical literature and other health information, applications that link biomedical knowledge from diverse primary sources (includes automated indexing), linking the genotype and phenotype, discovery, bionlp, medical informatics, biomedical informatics, disease networks, translational medicine, drug repositioning, rare diseases
الوصف: Objective: (1) To evaluate a state-of-the-art natural language processing (NLP)-based approach to automatically de-identify a large set of diverse clinical notes. (2) To measure the impact of de-identification on the performance of information extraction algorithms on the de-identified documents. Material and methods A cross-sectional study that included 3503 stratified, randomly selected clinical notes (over 22 note types) from five million documents produced at one of the largest US pediatric hospitals. Sensitivity, precision, F value of two automated de-identification systems for removing all 18 HIPAA-defined protected health information elements were computed. Performance was assessed against a manually generated ‘gold standard’. Statistical significance was tested. The automated de-identification performance was also compared with that of two humans on a 10% subsample of the gold standard. The effect of de-identification on the performance of subsequent medication extraction was measured. Results: The gold standard included 30 815 protected health information elements and more than one million tokens. The most accurate NLP method had 91.92% sensitivity (R) and 95.08% precision (P) overall. The performance of the system was indistinguishable from that of human annotators (annotators' performance was 92.15%(R)/93.95%(P) and 94.55%(R)/88.45%(P) overall while the best system obtained 92.91%(R)/95.73%(P) on same text). The impact of automated de-identification was minimal on the utility of the narrative notes for subsequent information extraction as measured by the sensitivity and precision of medication name extraction. Discussion and conclusion NLP-based de-identification shows excellent performance that rivals the performance of human annotators. Furthermore, unlike manual de-identification, the automated approach scales up to millions of documents quickly and inexpensively. ; Version of Record
نوع الوثيقة: article in journal/newspaper
وصف الملف: application/pdf
اللغة: English
تدمد: 1067-5027
العلاقة: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3555323/pdfTest/; Journal of the American Medical Informatics Association : JAMIA; Deleger, L., K. Molnar, G. Savova, F. Xia, T. Lingren, Q. Li, K. Marsolo, et al. 2012. “Large-scale evaluation of automated clinical note de-identification and its impact on information extraction.” Journal of the American Medical Informatics Association : JAMIA 20 (1): 84-94. doi:10.1136/amiajnl-2012-001012. http://dx.doi.org/10.1136/amiajnl-2012-001012Test.; http://nrs.harvard.edu/urn-3:HUL.InstRepos:11879366Test
DOI: 10.1136/amiajnl-2012-001012
الإتاحة: https://doi.org/10.1136/amiajnl-2012-001012Test
http://nrs.harvard.edu/urn-3:HUL.InstRepos:11879366Test
رقم الانضمام: edsbas.63D2D85E
قاعدة البيانات: BASE
الوصف
تدمد:10675027
DOI:10.1136/amiajnl-2012-001012